Staged Training Report ✓ Complete

Run ID: shoulder_session_multiheight_decoder_only
Generated: 2026-02-24 09:33:26
Stages Completed: 1
Total Elapsed Time: 10:58:13

Configuration

No config defaults changed since last commit.

All Staged Training Parameters (60 parameters)

Parameter	Value
`total_samples`	10000000
`batch_size`	8
`stage_samples_multiplier`	100000000000
`update_interval`	250
`window_size`	100
`num_best_models_to_keep`	1
`sampling_mode`	Loss-weighted
`loss_weight_temperature`	0.5
`loss_weight_refresh_interval`	50
`stop_on_divergence`	True
`divergence_gap`	0.002
`divergence_ratio`	1.5
`divergence_patience`	50
`divergence_min_updates`	10
`val_spike_threshold`	2.0
`val_spike_window`	15
`val_spike_frequency`	0.75
`val_plateau_patience`	250
`val_plateau_min_delta`	0.0001
`custom_lr`	0.0001
`disable_lr_scaling`	True
`custom_warmup`	-1
`lr_min_ratio`	0.001
`resume_warmup_ratio`	0.05
`plateau_factor`	0.8
`plateau_patience`	15
`preserve_optimizer`	False
`preserve_scheduler`	True
`samples_mode`	Train additional samples
`num_random_obs_to_visualize`	2
`selected_frame_offset`	3
`runs_per_stage`	5
`serial_runs`	True
`clean_old_checkpoints`	True
`enable_baseline`	False
`baseline_runs_per_stage`	1
`run_id`	shoulder_session_multiheight_decoder_only
`enable_wandb`	True
`wandb_project`	developmental-robot-movement
`lr_sweep.lr_min`	1e-07
`lr_sweep.lr_max`	0.01
`lr_sweep.phase_a_num_candidates`	5
`lr_sweep.phase_a_seeds`	1
`lr_sweep.phase_a_time_budget_min`	3.0
`lr_sweep.phase_a_survivor_count`	2
`lr_sweep.phase_b_seeds`	3
`lr_sweep.phase_b_time_budget_min`	10.0
`lr_sweep.ranking_metric`	median_best_val
`lr_sweep.min_samples_before_timeout`	1000
`lr_sweep.min_evals_before_stop`	5
`lr_sweep.save_sweep_state`	True
`plateau_sweep.enabled`	True
`plateau_sweep.plateau_ema_alpha`	0.85
`plateau_sweep.plateau_improvement_threshold`	0.0015
`plateau_sweep.plateau_patience`	25
`plateau_sweep.cooldown_updates`	5
`plateau_sweep.max_sweeps_per_stage`	2
`plateau_sweep.min_sweep_improvement`	0.0
`initial_sweep_enabled`	True
`stage_time_budget_min`	180

World Model Architecture (config.py)

Parameter	Value
`AUTOENCODER_LR`	0.0002
`BATCH_SIZE`	1
`CANVAS_HISTORY_SIZE`	3
`DECODER_ONLY_DEPTH`	10
`FOCAL_BETA`	5
`FOCAL_LOSS_ALPHA`	0.1
`FRAME_SIZE`	(224, 224)
`GRADIO_UPDATE_INTERVAL`	1
`LR_MIN_RATIO`	0.001
`MODEL_TYPE`	decoder_only
`PATCH_SIZE`	16
`PERCEPTUAL_LOSS_WEIGHT`	0
`SEPARATOR_WIDTH`	16
`WARMUP_STEPS`	1000
`WEIGHT_DECAY`	0.01

Timing Summary

Stage	Plateau Sweeps	Sweep Time	Training Time	Stage Total
Stage 1	5	01:24:03	00:14:35	01:38:39
TOTAL	5	01:24:03	00:14:35	01:38:39

Initial LR Sweep: Stage 1: selected LR 3.16e-05 in 00:15:19

Plateau Sweep Details

Total Sweeps: 5
Stages with Sweeps: 1 of 1
Total Sweep Time: 01:24:03
Average Sweep Duration: 00:16:48

Stage 1: 5 sweeps

LR Progression: 3.2e-05 → 3.2e-05 → 3.2e-05 → 3.2e-05 → 3.2e-05 → 1.8e-06

Sweep #	Triggered At (samples)	Wall Time	Selected LR	Duration
1	16,640	00:02:59	3.16e-05	00:17:02
2	42,752	00:24:40	3.16e-05	00:17:07
3	49,920	00:43:05	3.16e-05	00:15:59
4	56,576	01:00:16	3.16e-05	00:17:51
5	67,584	01:20:07	1.78e-06	00:16:03

Stage Results

Stage	Best Loss	Stop Reason	Samples Trained	Time	Sweeps	LR (Initial→Final)
Stage 1	0.034907	max_sweeps (2)	13,568	01:38:39	5	3.2e-05→1.8e-06

Total Plateau Sweeps: 5

Stop Reason Breakdown

max_sweeps: 1 stages

Loss Across Full Training Run

Loss Detail (Post Initial Drop)

Multi-Run Statistics

Total Runs: 5
Average Best Loss: 0.041099 ± 0.005503
Best Overall: 0.034907
Worst Overall: 0.050926

Stage 1 (5 runs)

Run	Best Loss	Stop Reason	Samples	Time	Selected
1	0.038307	max_sweeps (2)	6,912	02:26:54
2	0.050926	max_sweeps (2)	6,656	02:29:35
3	0.038634	max_sweeps (2)	9,728	02:11:44
4	0.034907	max_sweeps (2)	13,568	01:38:39	✓
5	0.042724	max_sweeps (2)	12,800	01:53:34
Mean: 0.041099 ± 0.005503		Min: 0.034907 / Max: 0.050926		Range: 0.016019

Best Checkpoint

Name: best_model_auto_session_so101_multiheight_part1_1345_shoulder_session_multiheight_decoder_only_00071424_cont_val_0.034907.pth
Stage: 1
Hybrid Loss (full session): 0.047490

Learning Rate Timeline with Plateau Sweeps

Stage Progression

Stage	Orig Loss	Train Loss	Time	Samples	Stop Reason
1 ⭐	0.047490	0.034907	01:38:39	13568	max_sweeps (2)

Hybrid Loss Over Original Session (per Stage)

Stage 1 (Best) - Hybrid Loss: 0.047490

Sample Counts

Cumulative Across All Stages

Per Stage

Stage 1 (Best) - Total Samples: 13,568

Best Checkpoint Inference

Selected Frame 3

Action 0

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Random Observations

Observation 104

Action 0

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Action 2

Observation 658

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Individual Stage Reports

Stage 1 Run 4 (Staged) (orig loss: 0.047490) ⭐